---
title: "Final Project"
author: "Jamie Zhang"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: sketchy
      navbar-bg: "#42033D"
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(flexdashboard)
library(tidyverse)
library(plotly)
library(DT)
library(dplyr)
library(ggplot2)
pm2516<-read_csv("PM2.5_2016.csv") %>% mutate(Year = "2016")
pm2517<-read_csv("PM2.5_2017.csv") %>% mutate(Year = "2017")
pm2518<-read_csv("PM2.5_2018.csv") %>% mutate(Year = "2018")
pm2519<-read_csv("PM2.5_2019.csv") %>% mutate(Year = "2019")
pm2520<-read_csv("PM2.5_2020.csv") %>% mutate(Year = "2020")
pm2521<-read_csv("PM2.5_2021.csv") %>% mutate(Year = "2021")
pm2522<-read_csv("PM2.5_2022.csv") %>% mutate(Year = "2022")
pm2523<-read_csv("PM2.5_2023.csv") %>% mutate(Year = "2023")
pm2524<-read_csv("PM2.5_2024.csv") %>% mutate(Year = "2024")

pm2516<-select(pm2516,-'County FIPS Code')
pm2517<-select(pm2517,-'County FIPS Code')
pm2518<-select(pm2518,-'County FIPS Code')
pm2519<-select(pm2519,-'County FIPS Code')
pm2520<-select(pm2520,-'County FIPS Code')
pm2521<-select(pm2521,-'County FIPS Code')
pm2522<-select(pm2522,-'County FIPS Code')
pm2523<-select(pm2523,-'County FIPS Code')
pm2524<-select(pm2524,-'County FIPS Code')

PM25<-bind_rows(pm2516,pm2517,pm2518,pm2519,pm2520,pm2521,pm2522,pm2523,pm2524)

```

Overview
===
This project explores the impact of the <span Style="color:#854798">COVID-19 pandemic</span> on PM2.5 air pollution levels in <span Style="color:#854798">Dayton, Ohio</span>, over the period from <span Style="color:#854798">2016 to 2024</span>. PM2.5 refers to fine particulate matter that can pose significant health risks due to its ability to penetrate deep into the lungs and bloodstream. The COVID-19 pandemic, particularly during its early stages, led to widespread <span Style="color:#854798">shutdowns</span> and <span Style="color:#854798">reduced human activity</span>, which may have influenced local air quality. By analyzing PM2.5 data across this time span, this study aims to identify any notable changes in pollution levels during the pandemic years. In addition, the project will investigate whether <span Style="color:#854798">seasonal or cyclical patterns</span> exist in PM2.5 concentrations, providing a broader understanding of how air quality in Dayton fluctuates throughout the year.


Data
===

Column {data-width=350}
---
Here is the data that I will be using to answer my questions about PM 2.5 particles in the air.

Column {.tabset data-width=550}
---

### 2016
```{r}
coln<-colnames(pm2516)
datatable(pm2516[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2017
```{r}
datatable(pm2517[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2018
```{r}
datatable(pm2518[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2019
```{r}
datatable(pm2519[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2020
```{r}
datatable(pm2520[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2021
```{r}
datatable(pm2521[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2022
```{r}
datatable(pm2522[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2023
```{r}
datatable(pm2523[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```

### 2024
```{r}
datatable(pm2524[1:500,],rownames = F, colnames = coln, options = list(pageLength = 20))
```


Yearly Averages
===